AIMC Topic: ROC Curve
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Deep learning-based prediction of autoimmune diseases.
Scientific reports
Feb 7, 2025
Autoimmune Diseases are a complex group of diseases caused by the immune system mistakenly attacking body tissues. Their etiology involves multiple factors such as genetics, environmental factors, and abnormalities in immune cells, making prediction ...
Artificial intelligence: a useful tool in active tuberculosis screening among vulnerable groups in Romania - advantages and limitations.
Frontiers in public health
Feb 7, 2025
INTRODUCTION: Despite advances in diagnostic technologies for tuberculosis (TB), global control of this disease requires improved technologies for active case finding in selected vulnerable populations. The integration of artificial intelligence (AI)...
Diagnosis of Sacroiliitis Through Semi-Supervised Segmentation and Radiomics Feature Analysis of MRI Images.
Journal of magnetic resonance imaging : JMRI
Feb 6, 2025
BACKGROUND: Sacroiliitis is a hallmark of ankylosing spondylitis (AS), and early detection plays an important role in managing the condition effectively. MRI is commonly used for diagnosing sacroiliitis, traditional methods often depend on subjective...
Adult
Sensitivity and Specificity
Radiomics
Male
Middle Aged
Female
Reproducibility of Results
Aged
Young Adult
Magnetic Resonance Imaging
Image Interpretation, Computer-Assisted
Edema
Sacroiliac Joint
Sacroiliitis
Adolescent
Child
Humans
ROC Curve
Algorithms
Image Processing, Computer-Assisted
Retrospective Studies
Excited state kinetics of tryptophan and NAD(P)H in blood plasma of normal and abnormal liver conditions: A tool to understand the metabolic changes and classification.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Feb 6, 2025
Early diagnosis at the metabolomic level is crucial for the treatment of liver cirrhosis and hepatocellular carcinoma (HCC). In this study, attempts were made to investigate the excited-state kinetics of intrinsic fluorophores, tryptophan and nicotin...
Integrating manual annotation with deep transfer learning and radiomics for vertebral fracture analysis.
BMC medical imaging
Feb 6, 2025
BACKGROUND: Vertebral compression fractures (VCFs) are prevalent in the elderly, often caused by osteoporosis or trauma. Differentiating acute from chronic VCFs is vital for treatment planning, but MRI, the gold standard, is inaccessible for some. Ho...
Habitat-Based Radiomics for Revealing Tumor Heterogeneity and Predicting Residual Cancer Burden Classification in Breast Cancer.
Clinical breast cancer
Feb 4, 2025
PURPOSE: To investigate the feasibility of characterizing tumor heterogeneity in breast cancer ultrasound images using habitat analysis technology and establish a radiomics machine learning model for predicting response to neoadjuvant chemotherapy (N...
Breast cancer image classification based on H&E staining using a causal attention graph neural network model.
Medical & biological engineering & computing
Feb 4, 2025
Breast cancer image classification remains a challenging task due to the high-resolution nature of pathological images and their complex feature distributions. Graph neural networks (GNNs) offer promising capabilities to capture local structural info...
Machine learning for predicting severe dengue in Puerto Rico.
Infectious diseases of poverty
Feb 4, 2025
BACKGROUND: Distinguishing between non-severe and severe dengue is crucial for timely intervention and reducing morbidity and mortality. World Health Organization (WHO)-recommended warning signs offer a practical approach for clinicians but have limi...
Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer.
Scientific reports
Feb 4, 2025
Gastric cancer (GC) is one of the most common tumors; one of the reasons for its poor prognosis is that GC cells can resist normal cell death process and therefore develop distant metastasis. Cuproptosis is a novel type of cell death and a limited nu...
Diagnostic Value of Median Nerve Cross-sectional Area Measured by Ultrasonography for the Severity of Carpal Tunnel Syndrome: A Machine Learning-Based Approach.
American journal of physical medicine & rehabilitation
Feb 3, 2025
OBJECTIVE: This study was conducted to evaluate the diagnostic performance and to establish cutoff values of median nerve cross-sectional area for classifying the severity of carpal tunnel syndrome.
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